1 Results

1.1 1-d Heatmap

1.2 Location meda_plots

1.3 Outliers as given by randomForest

1.4 Correlation Matrix

1.5 Cumulative Variance with Elbows

1.6 Paired Hex-binned plot

1.7 Hierarchical GMM Classifications

1.8 Hierarchical GMM Dendrogram

1.9 Stacked Means

1.10 Cluster Means

2 Restricting hGMM to \(K = 2\)

Here we are restricting hierarchical GMM to only go through on level. We are comparing the cluster results to the glut labels.

set.seed(314)
h2 <- hmc(sdat, maxDepth = 2, ccol = ccol, maxDim = 12)
h2lab <- viridis(max(h2$dat$labels$col))
stackM(h2, ccol = ccol, centered = TRUE, depth = 1)

pairs(h2$dat$data, pch = 19, cex = 0.2, col = c("blue", "red")[gabaID$gaba+1])

t1 <- table(truth = truth, pred = h2$dat$labels$col - 1)

(TP <- t1[1])
## [1] 647
(TN <- t1[4])
## [1] 40
(FP <- t1[2])
## [1] 45
(FN <- t1[3])
## [1] 61
Pos <- TP + FN
Neg <- TN + FP
 
(sens <- (TP/Pos))
## [1] 0.9138418
(spec <- TN/Neg)
## [1] 0.4705882
(ACC <- (TP + TN)/(TP + FP + FN + TN))
## [1] 0.8663304